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Infrastructure for AI

From GPUs to AI infrastructure.

Turn accelerated infrastructure into governed, consumable AI services. Build dedicated, shared or sovereign AI environments, from a single GPU instance to complete AI clusters, on infrastructure you control.

Explore Infrastructure for AI View architecture

AI-ready
infrastructure
Governed
and secure
Scales from pilot
to production
Your data.
Your control.

The challenge

AI needs more than GPUs.

Deploying GPU servers is only the beginning. Production AI needs compute, storage, high-speed networking, orchestration, isolation, monitoring, access control and lifecycle management working together.

Accelerated infrastructure

GPU servers
CPU compute
High-speed network
NVMe storage
Object storage
Kubernetes

Infrastructure for AI

Pool resources
Orchestrate workloads
Isolate environments
Govern access and usage
Scale on demand

AI-ready services

GPU virtual machines
Dedicated GPUs
GPU clusters
AI Kubernetes
AI development environments
Training and inference infrastructure

OutcomeAccelerated hardware becomes a governed AI infrastructure service.

The AI infrastructure stack

Every layer AI workloads need.

A complete, integrated stack to run AI workloads at scale, from accelerated compute to the workloads themselves, with governance built in.

Explore the architecture

AI workloadsTrain · Fine-tune · Infer · Develop · Experiment
AI servicesGPU compute · GPU clusters · AI Kubernetes · Model storage · Inference
AI orchestrationScheduling · GPU allocation · Placement · Quotas · Scaling · Lifecycle
Accelerated dataNVMe · Block · Object · File · Datasets · Backup
AI network fabricHigh-speed networking · Private networks · Load balancing · Segmentation
Accelerated computeGPU · CPU · Memory · Bare metal

Key capabilities

Infrastructure designed around the AI lifecycle.

From development to production, Oasis Stack provides the building blocks to run AI workloads efficiently, securely and at scale.

01
Accelerated Compute
GPU compute, GPU bare metal, GPU clusters and CPU compute, matched to the workload.
02
GPU Resource Pools
GPUs organised by type, performance class, location and purpose, then sold as services.
03
Estate & Unit Isolation
Dedicated environments for AI teams, workloads and data, each with its own boundary.
04
AI Kubernetes
GPU-enabled clusters with worker pools, scheduling, private networking and autoscaling.
05
Data Infrastructure
NVMe, file, object and backup tiers that keep accelerated compute fed.
06
Monitoring & Operations
Visibility, metering, quotas and lifecycle management across AI capacity.

AI infrastructure as a service

Consume the capability. Not the hardware complexity.

Physical GPUs are organised into pools and presented as defined services, so customers never choose a server. Everything is metered from the first GPU-hour.

GPU resource pools

Turn GPUs into consumable capacity.

AI performance pool

H200
H100
A100
L40

Catalogue

AI-GPU-SShared
AI-GPU-MShared
AI-GPU-LShared
Dedicated GPUReserved
GPU Bare MetalDirect

Pools are defined by GPU type, performance class, location, availability, workload purpose and commercial tier. The exact GPU models depend on each deployment; this pool is an example.

The AI catalogue

Instead of ordering ~~"2 × servers with 8 × H200 GPUs"~~, teams order the service the workload needs.

GPU COMPUTEDevelopment and individual accelerated workloads
DEDICATED GPUDedicated capacity for predictable performance
GPU BARE METALDirect access for demanding workloads
GPU CLUSTERMulti-node accelerated infrastructure for training
AI KUBERNETESContainers with GPU-enabled worker pools
AI ENVIRONMENTCompute, storage, network and Kubernetes together

Estate and unit isolation

Give every AI workload its own operating boundary.

AI infrastructure follows the same Organisation → Estate → Unit → Resources model as the rest of Oasis Stack, so access, quotas, policies and consumption are controlled at the right level.

OrganisationAcme Corporation EstateAI Estate

Model Development Unit

Experiment and build.

GPU VM
Dev environment
Object storage

Training Unit

Run accelerated workloads.

8 × GPU
Kubernetes
NVMe
Object storage

Production Inference Unit

Serve production workloads.

2 × GPU
Load balancer
Kubernetes
Monitoring

OutcomeDevelopment, training and production share a platform without losing operational separation. Example shown.

AI Kubernetes and data

Orchestrate at scale. Feed compute with the right data.

AI performance is not set by GPU capacity alone. Teams receive AI-ready Kubernetes and the storage tier each stage needs, without assembling the infrastructure themselves.

AI Kubernetes

An AI-ready cluster, delivered as a service.

AI Estate

AI Kubernetes cluster · control plane

GPU worker poolCPU worker pool

Persistent storage · Object storage · Private network · Load balancing · Monitoring

GPU-enabled worker nodes
Dedicated GPU pools
Cluster templates
Workload scheduling
Autoscaling

Data tiers

Data needs to move as fast as compute.

High-performanceNVMe · Premium blockTraining, scratch data, high-I/O workloads
Shared dataFile storageShared datasets and collaborative workloads
DatasetsObject storageTraining data, model artefacts, unstructured data
ProtectionArchive · BackupModel protection and dataset retention

Dataset · Object
Training · NVMe + GPU cluster
Model · Model storage
Inference · GPU compute
Archive · Backup

Pipelines and stores stay in the region the data was collected in; nothing leaves the cloud unless policy allows it.

From development to inference

One foundation. The complete AI lifecycle.

Use the same platform from experimentation to production, with consistent governance and operational control.

Same platform.
Further possibilities.

01DevelopExperiment and buildGPU VM · Dev environment · Object storage
02TrainRun accelerated workloadsGPU cluster · NVMe · High-speed network
03ValidateTest models and applicationsIsolated unit · GPU compute · Monitoring
04DeployMove to productionKubernetes · GPU pool · Private network
05InferServe at scaleGPU compute · Load balancer · Autoscaling
06OperateMonitor and optimiseObservability · Metering · Governance · Backup

The infrastructure model stays the same as workloads move from experimentation to production.

Built for operators, ready for AI teams

Put scarce GPU capacity where it creates value.

GPUs are costly and finite. Entitlements, quotas, scheduling and policies decide who gets what, and operators see allocated, available, reserved and consumed capacity at a glance. Valuable even where the customer already owns the GPUs.

For operatorsControl accelerated infrastructure.

Pool GPU capacity and define GPU classes
Create AI services
Set quotas and control access
Manage estates and units
Monitor capacity and utilisation
Track consumption per GPU-hour
Apply governance

GPU capacityExample

24Allocated
8Available

Training Unit16

Research Unit8

Inference Unit4

Available pool4

For AI teamsConsume what the workload needs.

Select estate
Select unit
Choose AI service
Select GPU or compute class
Attach storage
Configure network
Deploy

Launch AI environment

EstateAI Estate

UnitTraining Unit

ServiceGPU Cluster

GPU class8 GPUs

Storage2 TB NVMe

Deploy environment

GPU availability
GPU allocation
GPU utilisation
Compute and memory utilisation
Storage capacity
Network usage
Cluster health
Estate and unit consumption

How the modules work together

One request. A complete AI environment.

A single order is coordinated across the platform: Oasis Core runs the business side, Infrastructure for AI the accelerated side, Cloud Foundation the infrastructure services.

The customer requests

AI training environment

8 × GPU
2 TB NVMe
20 TB object storage
AI Kubernetes
Private network

Example request.

OASIS COREOrganisation · Estate · Unit · Identity · Entitlement · Workflow

then

INFRASTRUCTURE FOR AIGPU allocation · AI orchestration · Workload environment

then

CLOUD FOUNDATIONCompute · Storage · Network · Kubernetes

on

PHYSICAL INFRASTRUCTUREGPU servers · Storage · Network

Deploy anywhere

Build AI where the data needs to stay.

Choose where AI runs without changing how AI infrastructure is operated. Training data, models and the GPUs they run on stay in your cloud, under your governance.

Data stays in the region it was collected in
Weights, registries and endpoints stay inside your estate
GPU quotas, reservations and queues you can plan
GPU-hours and requests metered from the first minute

Private AIEnterprise-controlled environments
Sovereign AIDefined jurisdiction and governance
Service Provider AICommercial GPU and AI services
Federated AISelected capacity across Oasis clouds
Air-Gapped AINo external connectivity needed
Hybrid / Multi-Cloud AIAcross Oasis Stack and external clouds

AI without borders.With full control.

Workspaces and training

From a notebook to a production model.

Two products sit on the module, and your teams keep the frameworks they already use.

AI Factory

A training environment for national AI initiatives, with data, compute and model weights under your jurisdiction.

WorkspaceAI

GPU-enabled virtual desktops, notebooks and development environments for distributed teams, with governance built in.

Your frameworks

PyTorch, TensorFlow, JAX and Triton, from notebook to deployed service without re-architecting.

See it running

From a training job to a live endpoint.

The operator view of GPU pools and jobs, and a workload trained and served on the platform.

Module screenshot

[Infrastructure for AI console: GPU pools, jobs, endpoints and utilisation]

16:9 · 2560×1440 · PNG or WebP · the module’s own screen

Video

[A training job scheduled on GPU pools, the model registered and deployed as an endpoint]

16:9 · 1920×1080 · MP4 or WebM · no baked-in text or logo

Connected across Oasis Stack

AI infrastructure becomes part of the platform.

Infrastructure for AI is delivered through Oasis Core, runs on Cloud Foundation and is governed like every other service. It is not a standalone GPU cloud.

Oasis Core runs the cloud. Cloud Foundation builds it. Infrastructure for AI accelerates it.

Infrastructure for AI

Oasis CoreOrganisations, identity, lifecycle Cloud FoundationCompute, storage, network GovernancePolicy, security, compliance CloudSouqPublish AI services Digital TransformationApplications consuming AI Oasis Multi-CloudExternal clouds in context FederationShare selected capacity

Intelligence across the platformAmin for operators, Noura for customers

Adopting this module

What adopting Infrastructure for AI involves.

Each of these is agreed with you before deployment, and the walkthrough we show uses your selected services and integrations.

Talk to us

Workload compatibilityConfirm GPU models, drivers, frameworks, scheduling and supported sharing modes.
Data and accessAgree training-data locations, model access, isolation and permitted data transfers.
Service economicsDefine capacity, metering units, quotas and availability objectives per offering.

In productionDelivered on the same platform CloudHost runs YallaCloud on, governed and billed like every other service.

Read the case study

The outcome

Accelerated infrastructure becomes an AI-ready service.

Pool expensive capacityBring GPU, compute, storage and network into one operating model.
Deliver AI-ready environmentsCombine infrastructure into services designed for AI workloads.
Govern every workloadApply Organisation → Estate → Unit → Resources across AI infrastructure.
Scale without rebuildingAdd GPUs, clusters and locations while keeping the same service experience.

Oasis StackA CloudHost Initiative

More intelligence.
More opportunities.

Talk to us Oasis Sovereign Cloud